add docstring

pytorch_lightning/accelerators/accelerator.py

@@ -481,6 +481,12 @@ def connect_precision_plugin(self, plugin: PrecisionPlugin) -> None:
         self.setup_precision_plugin(plugin)
 
     def save_checkpoint(self, checkpoint: Dict[str, Any], filepath) -> None:
+        """Save model/training states as a checkpoint file through state-dump and file-write.
+
+        Args:
+            checkpoint: dict containing model and trainer state
+            filepath: write-target file's path
+        """
         self.training_type_plugin.save_checkpoint(checkpoint, filepath)
 
     @property

pytorch_lightning/plugins/training_type/tpu_spawn.py

@@ -300,9 +300,8 @@ def save_checkpoint(self, checkpoint: Dict[str, Any], filepath: str) -> None:
         """Save model/training states as a checkpoint file through state-dump and file-write.
 
         Args:
-            trainer: PyTorch Lightning Trainer
+            checkpoint: dict containing model and trainer state
             filepath: write-target file's path
-            weights_only: saving model weights only
         """
         # Todo: TypeError: 'mappingproxy' object does not support item assignment
         self.save({k: v for k, v in checkpoint.items() if k != "callbacks"}, filepath)

pytorch_lightning/plugins/training_type/training_type_plugin.py

@@ -198,6 +198,12 @@ def setup_optimizers_in_pre_dispatch(self) -> bool:
         return False
 
     def save_checkpoint(self, checkpoint: Dict[str, Any], filepath: str) -> None:
+        """Save model/training states as a checkpoint file through state-dump and file-write.
+
+        Args:
+            checkpoint: dict containing model and trainer state
+            filepath: write-target file's path
+        """
         # dump states as a checkpoint dictionary object
         if self.is_global_zero:
             checkpoint = self.on_save(checkpoint)